Wpływ tributylocyny na przyjmowanie pokarmu i ekspresję neuropeptydów w mózgu szczurów

Introduction: Tributyltin (TBT) is a largely diffused environmental pollutant. Several studies have demonstrated that TBT is involved in the development of obesity. However, few studies addressing the effects of TBT on the brain neuropeptides involved in appetite and body weight homeostasis have been published.Material and methods: Experiments were carried out on female and male Sprague-Dawley rats. Animals were exposed to TBT (0.5 μg/kg body weight) for 54 days. The hepatic triglyceride and total cholesterol were determined using commercial enzyme kits. The NPY, AgRP, POMC and CART mRNA expression in brains were quantified by real-time PCR.Results: TBT exposure resulted in significant increases in the hepatic total cholesterol and triglyceride concentration of both male and female rats. Interestingly, increases in body weight and fat mass were only found in the TBT-treated male rats. TBT exposure also led to a significant increase in food intake by the female rats, while no change was observed in the male rats. Moreover, the neuropeptides expression was different between males and females after TBT exposure. TBT induced brain NPY expression in the female rats, and depressed brain POMC, AgRP and CART expression in the males.Conclusions: TBT can increase food intake in female rats, which is associated with the disturbance of NPY in brains. TBT had sex-different effects on brain NPY, AgRP, POMC and CART mRNA expression, which indicates a complex neuroendocrine mechanism of TBT. (Endokrynol Pol 2014; 65 (6): 485–490)Wstęp: Tributylocyna (TBT) jest powszechnie występującym w środowisku zanieczyszczeniem. Prowadzone dotychczas badania wykazały, że obecność TBT może mieć związek z rozwojem otyłości. Niewiele jest jednak doniesień na temat wpływu TBT na układ neuropeptydów w mózgowiu regulujących łaknienie i utrzymanie masy ciała. Materiał i metody: Doświadczenia przeprowadzono na szczurach obu płci szczepu Sprague-Dawley. Zwierzętom podawano przez 54 dni TBT w dawce 0,5 μg/kg masy ciała. Stężenie triglicerydów i całkowite stężenie cholesterolu w wątrobie oznaczano przy użyciu komercyjnych zestawów analitycznych. Obecność mRNA NPY, AgRP, POMC i CART w mózgach szczurów oznaczano metodą PCR w czasie rzeczywistym (real time-PCR).Wyniki: Ekspozycja na TBT powodowała istotne zwiększenie całkowitego stężenia cholesterolu i trójglicerydów w wątrobie zarówno samców, jak i samic szczura. Co ciekawe, zwiększenie masy ciała i masy tkanki tłuszczowej odnotowano jedynie u samców, którym podawano TBT. Stwierdzono także istotne zwiększenie ilości pokarmu przyjmowanego przez samice, natomiast nie obserwowano takich zmian u samców. Ponadto, odnotowano różnice w ekspresji neuropeptydów w mózgowiu samic i samców szczura, którym podawano TBT. Ekspozycja na TBT nasilała ekspresję NPY w mózgach samic, ale równocześnie zmniejszała ekspresję POMC, AgRP i CART w mózgach samców szczura.Wnioski: Ekspozycja na TBT może zwiększać ilość pokarmu spożywanego przez samice szczura, co wiąże się z zaburzeniem układu NPY w mózgowiu. Trybutylocyna wywiera odmienny wpływ na ekspresję mRNA NPY, AgRP, POMC i CART w mózgach samców i samic szczura, co wskazuje na istnienie złożonego mechanizmu działania tej substancji na układ neuroendokrynny. (Endokrynol Pol 2014; 65 (6): 485–490

Zero-Shot Image Harmonization with Generative Model Prior

We propose a zero-shot approach to image harmonization, aiming to overcome the reliance on large amounts of synthetic composite images in existing methods. These methods, while showing promising results, involve significant training expenses and often struggle with generalization to unseen images. To this end, we introduce a fully modularized framework inspired by human behavior. Leveraging the reasoning capabilities of recent foundation models in language and vision, our approach comprises three main stages. Initially, we employ a pretrained vision-language model (VLM) to generate descriptions for the composite image. Subsequently, these descriptions guide the foreground harmonization direction of a text-to-image generative model (T2I). We refine text embeddings for enhanced representation of imaging conditions and employ self-attention and edge maps for structure preservation. Following each harmonization iteration, an evaluator determines whether to conclude or modify the harmonization direction. The resulting framework, mirroring human behavior, achieves harmonious results without the need for extensive training. We present compelling visual results across diverse scenes and objects, along with a user study validating the effectiveness of our approach.Comment: Code Page: https://github.com/WindVChen/Diff-Harmonization. In paper-v2, we introduce multiple new designs for solving previous limitation

Dense Pixel-to-Pixel Harmonization via Continuous Image Representation

High-resolution (HR) image harmonization is of great significance in real-world applications such as image synthesis and image editing. However, due to the high memory costs, existing dense pixel-to-pixel harmonization methods are mainly focusing on processing low-resolution (LR) images. Some recent works resort to combining with color-to-color transformations but are either limited to certain resolutions or heavily depend on hand-crafted image filters. In this work, we explore leveraging the implicit neural representation (INR) and propose a novel image Harmonization method based on Implicit neural Networks (HINet), which to the best of our knowledge, is the first dense pixel-to-pixel method applicable to HR images without any hand-crafted filter design. Inspired by the Retinex theory, we decouple the MLPs into two parts to respectively capture the content and environment of composite images. A Low-Resolution Image Prior (LRIP) network is designed to alleviate the Boundary Inconsistency problem, and we also propose new designs for the training and inference process. Extensive experiments have demonstrated the effectiveness of our method compared with state-of-the-art methods. Furthermore, some interesting and practical applications of the proposed method are explored. Our code is available at https://github.com/WindVChen/INR-Harmonization.Comment: Accepted by IEEE Transactions on Circuits and Systems for Video Technology (TCSVT

Group Work Involved in the Practice of Life Education

With the continuous advancement of material civilization, the life values of college students appeared alienation. More and more students did not know how to face pressure and seek help; someone was to end their lives to solve the problem. Obliviously, life education was the urgent and necessary need in China. Therefore, the author explored the feasibility of life education from the angle of social work practice (such as the “group work”). Key words: Life education; Group work; College studentsResumé: Avec l'avancement continu de civilisation matérielle, les valeurs de vie d'étudiants universitaires ont apparu l'aliénation. De plus en plus les étudiants n'ont pas su comment faire face à la pression et chercher l'aide; quelqu'un devait finir leurs vies pour résoudre le problème. Apparemment, l'enseignement de vie était le besoin urgent et nécessaire en Chine. Donc, l'auteur a exploré la faisabilité d'enseignement de vie sous un angle de pratique (comme "le travail de groupe").Mots-clés: Enseignement de vie; Travail de groupe; Etudiants universitaire

CDMamba: Remote Sensing Image Change Detection with Mamba

Recently, the Mamba architecture based on state space models has demonstrated remarkable performance in a series of natural language processing tasks and has been rapidly applied to remote sensing change detection (CD) tasks. However, most methods enhance the global receptive field by directly modifying the scanning mode of Mamba, neglecting the crucial role that local information plays in dense prediction tasks (e.g., CD). In this article, we propose a model called CDMamba, which effectively combines global and local features for handling CD tasks. Specifically, the Scaled Residual ConvMamba (SRCM) block is proposed to utilize the ability of Mamba to extract global features and convolution to enhance the local details, to alleviate the issue that current Mamba-based methods lack detailed clues and are difficult to achieve fine detection in dense prediction tasks. Furthermore, considering the characteristics of bi-temporal feature interaction required for CD, the Adaptive Global Local Guided Fusion (AGLGF) block is proposed to dynamically facilitate the bi-temporal interaction guided by other temporal global/local features. Our intuition is that more discriminative change features can be acquired with the guidance of other temporal features. Extensive experiments on three datasets demonstrate that our proposed CDMamba outperforms the current state-of-the-art methods. Our code will be open-sourced at https://github.com/zmoka-zht/CDMamba

Continuous Cross-resolution Remote Sensing Image Change Detection

Most contemporary supervised Remote Sensing (RS) image Change Detection (CD) approaches are customized for equal-resolution bitemporal images. Real-world applications raise the need for cross-resolution change detection, aka, CD based on bitemporal images with different spatial resolutions. Given training samples of a fixed bitemporal resolution difference (ratio) between the high-resolution (HR) image and the low-resolution (LR) one, current cross-resolution methods may fit a certain ratio but lack adaptation to other resolution differences. Toward continuous cross-resolution CD, we propose scale-invariant learning to enforce the model consistently predicting HR results given synthesized samples of varying resolution differences. Concretely, we synthesize blurred versions of the HR image by random downsampled reconstructions to reduce the gap between HR and LR images. We introduce coordinate-based representations to decode per-pixel predictions by feeding the coordinate query and corresponding multi-level embedding features into an MLP that implicitly learns the shape of land cover changes, therefore benefiting recognizing blurred objects in the LR image. Moreover, considering that spatial resolution mainly affects the local textures, we apply local-window self-attention to align bitemporal features during the early stages of the encoder. Extensive experiments on two synthesized and one real-world different-resolution CD datasets verify the effectiveness of the proposed method. Our method significantly outperforms several vanilla CD methods and two cross-resolution CD methods on the three datasets both in in-distribution and out-of-distribution settings. The empirical results suggest that our method could yield relatively consistent HR change predictions regardless of varying bitemporal resolution ratios. Our code is available at \url{https://github.com/justchenhao/SILI_CD}.Comment: 21 pages, 11 figures. Accepted article by IEEE TGR

RSCaMa: Remote Sensing Image Change Captioning with State Space Model

Remote Sensing Image Change Captioning (RSICC) aims to describe surface changes between multi-temporal remote sensing images in language, including the changed object categories, locations, and dynamics of changing objects (e.g., added or disappeared). This poses challenges to spatial and temporal modeling of bi-temporal features. Despite previous methods progressing in the spatial change perception, there are still weaknesses in joint spatial-temporal modeling. To address this, in this paper, we propose a novel RSCaMa model, which achieves efficient joint spatial-temporal modeling through multiple CaMa layers, enabling iterative refinement of bi-temporal features. To achieve efficient spatial modeling, we introduce the recently popular Mamba (a state space model) with a global receptive field and linear complexity into the RSICC task and propose the Spatial Difference-aware SSM (SD-SSM), overcoming limitations of previous CNN- and Transformer-based methods in the receptive field and computational complexity. SD-SSM enhances the model's ability to capture spatial changes sharply. In terms of efficient temporal modeling, considering the potential correlation between the temporal scanning characteristics of Mamba and the temporality of the RSICC, we propose the Temporal-Traversing SSM (TT-SSM), which scans bi-temporal features in a temporal cross-wise manner, enhancing the model's temporal understanding and information interaction. Experiments validate the effectiveness of the efficient joint spatial-temporal modeling and demonstrate the outstanding performance of RSCaMa and the potential of the Mamba in the RSICC task. Additionally, we systematically compare three different language decoders, including Mamba, GPT-style decoder, and Transformer decoder, providing valuable insights for future RSICC research. The code will be available at \emph{\url{https://github.com/Chen-Yang-Liu/RSCaMa}

EGCG Maintains Th1/Th2 Balance and Mitigates Ulcerative Colitis Induced by Dextran Sulfate Sodium through TLR4/MyD88/NF- κ

Objective. To observe the protective effect of epigallocatechin gallate (EGCG) on dextran sulfate sodium- (DSS-) induced ulcerative colitis in rats and to explore the roles of TLR4/MyD88/NF-κB signaling pathway. Methods. Rat models of ulcerative colitis were established by giving DSS. EGCG (50 mg/kg/d) was given to assess disease activity index. HE staining was applied to observe histological changes. ELISA and qPCR detected the expression of inflammatory factors. Flow cytometry was used to measure the percentage of CD4+IFN-γ+ and CD4+IL-4+ in the spleen and colon. TLR4 antagonist E5564 was given in each group. Flow cytometry was utilized to detect CD4+IFN-γ+ and CD4+IL-4+ cells. Immunohistochemistry, qPCR, and western blot assay were applied to measure the expression of TLR4, MyD88, and NF-κB. Results. EGCG improved the intestinal mucosal injury in rats, inhibited production of inflammatory factors, maintained the balance of Th1/Th2, and reduced the expression of TLR4, MyD88, and NF-κB. After TLR4 antagonism, the protective effect of EGCG on intestinal mucosal injury was weakened in rats with ulcerative colitis, and the expressions of inflammatory factors were upregulated. Conclusion. EGCG can inhibit the intestinal inflammatory response by reducing the severity of ulcerative colitis and maintaining the Th1/Th2 balance through the TLR4/MyD88/NF-κB signaling pathway